Java Converters.Mat_to_vector_vector_DMatch方法代碼示例

import org.opencv.utils.Converters; //導入方法依賴的package包/類
public  void knnMatch(Mat queryDescriptors, List<MatOfDMatch> matches, int k, List<Mat> masks, boolean compactResult)
{
    Mat matches_mat = new Mat();
    Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
    knnMatch_2(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, k, masks_mat.nativeObj, compactResult);
    Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
    matches_mat.release();
    return;
}
 

import org.opencv.utils.Converters; //導入方法依賴的package包/類
public  void radiusMatch(Mat queryDescriptors, Mat trainDescriptors, List<MatOfDMatch> matches, float maxDistance)
{
    Mat matches_mat = new Mat();
    radiusMatch_1(nativeObj, queryDescriptors.nativeObj, trainDescriptors.nativeObj, matches_mat.nativeObj, maxDistance);
    Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
    matches_mat.release();
    return;
}
 

import org.opencv.utils.Converters; //導入方法依賴的package包/類
public  void knnMatch(Mat queryDescriptors, Mat trainDescriptors, List<MatOfDMatch> matches, int k)
{
    Mat matches_mat = new Mat();
    knnMatch_1(nativeObj, queryDescriptors.nativeObj, trainDescriptors.nativeObj, matches_mat.nativeObj, k);
    Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
    matches_mat.release();
    return;
}
 

import org.opencv.utils.Converters; //導入方法依賴的package包/類
public  void radiusMatch(Mat queryDescriptors, List<MatOfDMatch> matches, float maxDistance, List<Mat> masks, boolean compactResult)
{
    Mat matches_mat = new Mat();
    Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
    radiusMatch_2(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, maxDistance, masks_mat.nativeObj, compactResult);
    Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
    matches_mat.release();
    return;
}
 

import org.opencv.utils.Converters; //導入方法依賴的package包/類
public  void knnMatch(Mat queryDescriptors, List<MatOfDMatch> matches, int k)
{
    Mat matches_mat = new Mat();
    knnMatch_3(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, k);
    Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
    matches_mat.release();
    return;
}
 

import org.opencv.utils.Converters; //導入方法依賴的package包/類
/**
* <p>Finds the k best matches for each descriptor from a query set.</p>
*
* <p>These extended variants of "DescriptorMatcher.match" methods find several
* best matches for each query descriptor. The matches are returned in the
* distance increasing order. See "DescriptorMatcher.match" for the details
* about query and train descriptors.</p>
*
* @param queryDescriptors Query set of descriptors.
* @param matches Matches. Each <code>matches[i]</code> is k or less matches for
* the same query descriptor.
* @param k Count of best matches found per each query descriptor or less if a
* query descriptor has less than k possible matches in total.
*
* @see <a href="http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_descriptor_matchers.html#descriptormatcher-knnmatch">org.opencv.features2d.DescriptorMatcher.knnMatch</a>
*/
   public  void knnMatch(Mat queryDescriptors, List<MatOfDMatch> matches, int k)
   {
       Mat matches_mat = new Mat();
       knnMatch_3(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, k);
       Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
       return;
   }
 

import org.opencv.utils.Converters; //導入方法依賴的package包/類
/**
* <p>For each query keypoint, finds the training keypoints not farther than the
* specified distance.</p>
*
* <p>The methods are similar to <code>DescriptorMatcher.radius</code>. But this
* class does not require explicitly computed keypoint descriptors.</p>
*
* @param queryImage a queryImage
* @param queryKeypoints a queryKeypoints
* @param matches a matches
* @param maxDistance a maxDistance
*
* @see <a href="http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_generic_descriptor_matchers.html#genericdescriptormatcher-radiusmatch">org.opencv.features2d.GenericDescriptorMatcher.radiusMatch</a>
*/
   public  void radiusMatch(Mat queryImage, MatOfKeyPoint queryKeypoints, List<MatOfDMatch> matches, float maxDistance)
   {
       Mat queryKeypoints_mat = queryKeypoints;
       Mat matches_mat = new Mat();
       radiusMatch_3(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, matches_mat.nativeObj, maxDistance);
       Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
       return;
   }
 

import org.opencv.utils.Converters; //導入方法依賴的package包/類
/**
* <p>For each query descriptor, finds the training descriptors not farther than
* the specified distance.</p>
*
* <p>For each query descriptor, the methods find such training descriptors that
* the distance between the query descriptor and the training descriptor is
* equal or smaller than <code>maxDistance</code>. Found matches are returned in
* the distance increasing order.</p>
*
* @param queryDescriptors Query set of descriptors.
* @param matches Found matches.
* @param maxDistance Threshold for the distance between matched descriptors.
* Distance means here metric distance (e.g. Hamming distance), not the distance
* between coordinates (which is measured in Pixels)!
*
* @see <a href="http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_descriptor_matchers.html#descriptormatcher-radiusmatch">org.opencv.features2d.DescriptorMatcher.radiusMatch</a>
*/
   public  void radiusMatch(Mat queryDescriptors, List<MatOfDMatch> matches, float maxDistance)
   {
       Mat matches_mat = new Mat();
       radiusMatch_3(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, maxDistance);
       Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
       return;
   }
 

import org.opencv.utils.Converters; //導入方法依賴的package包/類
/**
* <p>Finds the <code>k</code> best matches for each query keypoint.</p>
*
* <p>The methods are extended variants of <code>GenericDescriptorMatch.match</code>.
* The parameters are similar, and the semantics is similar to <code>DescriptorMatcher.knnMatch</code>.
* But this class does not require explicitly computed keypoint descriptors.</p>
*
* @param queryImage a queryImage
* @param queryKeypoints a queryKeypoints
* @param trainImage a trainImage
* @param trainKeypoints a trainKeypoints
* @param matches a matches
* @param k a k
* @param mask a mask
* @param compactResult a compactResult
*
* @see <a href="http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_generic_descriptor_matchers.html#genericdescriptormatcher-knnmatch">org.opencv.features2d.GenericDescriptorMatcher.knnMatch</a>
*/
   public  void knnMatch(Mat queryImage, MatOfKeyPoint queryKeypoints, Mat trainImage, MatOfKeyPoint trainKeypoints, List<MatOfDMatch> matches, int k, Mat mask, boolean compactResult)
   {
       Mat queryKeypoints_mat = queryKeypoints;
       Mat trainKeypoints_mat = trainKeypoints;
       Mat matches_mat = new Mat();
       knnMatch_0(nativeObj, queryImage.nativeObj, queryKeypoints_mat.nativeObj, trainImage.nativeObj, trainKeypoints_mat.nativeObj, matches_mat.nativeObj, k, mask.nativeObj, compactResult);
       Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
       matches_mat.release();
       return;
   }
 

import org.opencv.utils.Converters; //導入方法依賴的package包/類
/**
* <p>Finds the k best matches for each descriptor from a query set.</p>
*
* <p>These extended variants of "DescriptorMatcher.match" methods find several
* best matches for each query descriptor. The matches are returned in the
* distance increasing order. See "DescriptorMatcher.match" for the details
* about query and train descriptors.</p>
*
* @param queryDescriptors Query set of descriptors.
* @param matches Matches. Each <code>matches[i]</code> is k or less matches for
* the same query descriptor.
* @param k Count of best matches found per each query descriptor or less if a
* query descriptor has less than k possible matches in total.
* @param masks Set of masks. Each <code>masks[i]</code> specifies permissible
* matches between the input query descriptors and stored train descriptors from
* the i-th image <code>trainDescCollection[i]</code>.
* @param compactResult Parameter used when the mask (or masks) is not empty. If
* <code>compactResult</code> is false, the <code>matches</code> vector has the
* same size as <code>queryDescriptors</code> rows. If <code>compactResult</code>
* is true, the <code>matches</code> vector does not contain matches for fully
* masked-out query descriptors.
*
* @see <a href="http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_descriptor_matchers.html#descriptormatcher-knnmatch">org.opencv.features2d.DescriptorMatcher.knnMatch</a>
*/
   public  void knnMatch(Mat queryDescriptors, List<MatOfDMatch> matches, int k, List<Mat> masks, boolean compactResult)
   {
       Mat matches_mat = new Mat();
       Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
       knnMatch_2(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, k, masks_mat.nativeObj, compactResult);
       Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
       return;
   }
 

import org.opencv.utils.Converters; //導入方法依賴的package包/類
/**
* <p>For each query descriptor, finds the training descriptors not farther than
* the specified distance.</p>
*
* <p>For each query descriptor, the methods find such training descriptors that
* the distance between the query descriptor and the training descriptor is
* equal or smaller than <code>maxDistance</code>. Found matches are returned in
* the distance increasing order.</p>
*
* @param queryDescriptors Query set of descriptors.
* @param trainDescriptors Train set of descriptors. This set is not added to
* the train descriptors collection stored in the class object.
* @param matches Found matches.
* @param maxDistance Threshold for the distance between matched descriptors.
* Distance means here metric distance (e.g. Hamming distance), not the distance
* between coordinates (which is measured in Pixels)!
* @param mask Mask specifying permissible matches between an input query and
* train matrices of descriptors.
* @param compactResult Parameter used when the mask (or masks) is not empty. If
* <code>compactResult</code> is false, the <code>matches</code> vector has the
* same size as <code>queryDescriptors</code> rows. If <code>compactResult</code>
* is true, the <code>matches</code> vector does not contain matches for fully
* masked-out query descriptors.
*
* @see <a href="http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_descriptor_matchers.html#descriptormatcher-radiusmatch">org.opencv.features2d.DescriptorMatcher.radiusMatch</a>
*/
   public  void radiusMatch(Mat queryDescriptors, Mat trainDescriptors, List<MatOfDMatch> matches, float maxDistance, Mat mask, boolean compactResult)
   {
       Mat matches_mat = new Mat();
       radiusMatch_0(nativeObj, queryDescriptors.nativeObj, trainDescriptors.nativeObj, matches_mat.nativeObj, maxDistance, mask.nativeObj, compactResult);
       Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
       return;
   }